VLASE：基于语义边聚合的车辆定位
VLASE: Vehicle Localization by Aggregating Semantic Edges
摘要：
在本文中，我们提出了VLASE，一个框架，使用图像的语义边缘特征，以实现道路上的定位。语义边缘特征表示边缘轮廓，该边缘轮廓将诸如建筑物-天空、道路-人行道和建筑物-地面的不同对象对分开。虽然先前的工作已经显示出有希望的结果，利用突出的类，如天空和建筑物之间的边界使用天际线，我们推广这种方法来考虑语义边缘功能，从19个不同的类。我们的定位算法很简单，但非常强大。我们使用最近推出的CASENet架构提取语义边缘特征，并利用VLAD框架进行图像检索。我们的实验表明，我们实现了一些国家的最先进的定位算法，如SIFT-VLAD及其深度变体NetVLAD的改进。我们使用消融研究来研究不同语义类的重要性，并表明我们的统一方法相比于单个突出特征（如天际线）具有更好的性能。
英文摘要：
In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road- sidewalk, and building-ground. While prior work has shown promising results by utilizing the boundary between prominent classes such as sky and building using skylines, we generalize this approach to consider semantic edge features that arise from 19 different classes. Our localization algorithm is simple, yet very powerful. We extract semantic edge features using a recently introduced CASENet architecture and utilize VLAD framework to perform image retrieval. Our experiments show that we achieve improvement over some of the state-of-the-art localization algorithms such as SIFT-VLAD and its deep variant NetVLAD. We use ablation study to study the importance of different semantic classes and show that our unified approach achieves better performance compared to individual prominent features such as skylines.
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